Conference Agenda

This paper presents a hierarchical method for collapsing a polygon to point within a structured multi-scale representation. The approach is based on the straight skeleton, which drives the shrinking process through event-based transformations such as edge and split events. These events define how the polygon changes during collapse and produce a hierarchy of intermediate geometric states between the initial polygon and the final point.

The resulting hierarchy is integrated into a Dual Half-Edge (DHE) structure, where the primal space represents successive geometric states and the dual space represents the hierarchical relations between them. This produces a connected 2D+1D representation in which the third dimension corresponds to scale rather than physical height. The resulting model is interpreted as a LoD Transition Space (LTS), allowing the full polygon-to-point transition to be represented continuously across scale.

The proposed framework contributes to model-based multi-scale representation by explicitly linking geometric transformation, topological change, and hierarchical structure within a unified representation. In addition to its relevance for vario-scale cartography and generalisation, the method also has potential applicability in domains where gradual geometric transformation is required, such as procedural modeling, animation, and related geometric applications.

The CGCS2000 (China Geodetic Coordinate System 2000) reference framework, which has been employed since July 1, 2008 is based on the ITRF97 reference framework and only meets the application requirements of China's regional. With the sustained development of China's economy and society, and the globalization of the applications of BeiDou navigation satellite system (BDS), there is a need to establish global CGCS2000 reference framework. This paper studies mathematical method for construction Global CGCS2000 reference framework, the theory and algorithm of two-step method with the inner constraints theory is analysed. The constraint conditions of coordinate reference are redefined according to the minimum standard of frame transition parameters and rate variation. As a result, the adjusted network enjoys the highest degree of fitting to the shape of the initial network and maintain the inherent purity of the coordinate network using different observation technologies, this research result can improve the basic theory of terrestrial reference framework determination, and provide scientific methods for the globalization of the CGCS2000.

Interpreting multi-storey property rights is difficult when information is scattered across 2D plans and text or locked inside desktop projects. We present a web-based pathway that communicates strata lots and common property consistently across levels in a standard browser. Aligned with the 3D Cadastral Survey Data Model and Exchange (3D CSDM) of Australia, we propose an open-source, web-first approach. The method couples a lightweight browser viewer (level/tenure filters, plan overlay, search, readable legend) with an explicit conversion step that standardises common GIS inputs into a fixed core JSON profile, with limited official CSDM-aligned JSON-LD hooks applied only to selected keys that have exact matches in the published vocabularies. Using a New South Wales case study, we evaluated the viewer against ISO 9241-11 criteria (effectiveness, efficiency). Across repeated trials (cache disabled/enabled), mean page-open times were 0.60 s (Chrome) and 1.48 s (Edge); interaction averaged 50–60 FPS; level filters applied in 40–55 ms; all five tasks succeeded. Practically, this delivers fast, consistent 3D communication of lots and common property without installs, lowering access barriers for agencies and owners while aligning with 3D CSDM’s web-first direction. Next, we will finalise parity between Upload-and-View and the Reference Viewer and add a light in-viewer validation panel.

This research presents a methodology for conducting shadow geometric analysis, specifically the shadow boundary in an urban model. Input data include a georeferenced CityGML LoD2 and terrain model. Additional land cover data is used to exclude some parts of the model from analysis. Shadow computation is based on a sunray vector, which is computed based on the sun position on the given day and time. The geometry of original models are divided into parts classified as either exposed to the sun or shaded. It can be used for analytical purposes in other applications, such as urban planning, energy assessment, and photovoltaic potentiality analysis, by accurately identifying sunlit and shaded areas within 3D city models. The analysis is performed in the FME software package, which is a general-purpose ETL tool.

It is desirable that a 3D city model at level of detail 2 (LOD2) has texture images mapped on building surfaces. Owing to the cost of image collection, it would be the best way to use aerial images for texture mapping at present. Although aerial oblique images provide higher-resolution texture images, using aerial oblique images has a major issue of occlusion. Accordingly, we develop software for texture mapping to a 3D city model using aerial nadir and oblique images, aiming to minimize the impact of occlusion. The software designed to be used in ordinary operation includes the features of automatically detecting occlusions on building surfaces within images by utilizing the geometry of a 3D city model and automatically selecting appropriate oblique and nadir images for texture mapping. The major feature of the developed software is its ability to process grid by grid on a building surface. The validation experiment results confirm the software's satisfactory performance in practice. Moreover, the experiment results indicate that the performance of the software depends on the ability of a 3D city model to represent buildings. Since we have recognized that it would be effective if each pixel of a texture image has its own resolution, we plan to modify the software so that each pixel can have its own resolution.

Rapid urbanization in developing countries such as Iran has intensified pressure on agricultural land, highlighting the need for sustainable and efficient food production systems. Agricultural Plastic Greenhouses (APGs) have become a scalable alternative by enabling year-round cultivation and optimized land utilization. However, their rapid expansion necessitates continuous monitoring to evaluate structural integrity and environmental impacts, including soil degradation, plastic waste accumulation, and water consumption. This study presents a deep learning-based framework for the automated detection and condition assessment of APGs using 0.5~m resolution Google Earth imagery across four major agricultural regions in Tehran County: Pakdasht, Qarchak, Pishva, and Varamin. The proposed pipeline integrates YOLOv11 for precise APG segmentation with a U-Net architecture employing a MobileNetV2 backbone for classifying damaged and intact structures. Out of 158,912 analyzed image tiles, 6,835 contained APGs, covering an estimated area of 18.73~km\textsuperscript{2}. Among these, 1,863 damaged structures were identified, predominantly located in Pakdasht and Pishva. Around 20\% of the annotated greenhouses were verified on-site, improving labeling reliability, and the relatively standardized design of APGs in Iran suggests the model could generalize to similar regions, with minor fine-tuning using local samples if necessary. GIS-based spatial analysis further delineated potential plastic waste risk zones, supporting targeted environmental management. Comparison with government statistics and Sentinel-2 imagery from 2021 and 2024 revealed a continued shift toward greenhouse farming in response to declining cropland availability. The proposed framework provides a scalable and replicable tool for periodic APG monitoring, facilitating data-driven policymaking and sustainable agricultural planning.